Next Article in Journal
Levels and Sources of Atmospheric Particle-Bound Mercury in Atmospheric Particulate Matter (PM10) at Several Sites of an Atlantic Coastal European Region
Previous Article in Journal
The Effect of Technological Progress, Demand, and Energy Policy on Agricultural and Bioenergy Markets
Previous Article in Special Issue
Coupling of Soil Moisture and Air Temperature from Multiyear Data During 1980–2013 over China
Open AccessArticle

The Contribution Rate of Driving Factors and Their Interactions to Temperature in the Yangtze River Delta Region

by Cheng Zhou 1, Nina Zhu 2,3,4,*, Jianhua Xu 2,3,4,* and Dongyang Yang 5
1
Faculty of Culture and Tourism, Shanxi University of Finance and Economics, Taiyuan 030006, China
2
Key Laboratory of Geographic Information Science (Ministry of Education), East China Normal University, Shanghai 200241, China
3
Research Center for East–West Cooperation in China, East China Normal University, Shanghai 200241, China
4
School of Geographic Sciences, East China Normal University, Shanghai 200241, China
5
Key Research Institute of Yellow River Civilization and Sustainable Development, Henan University, Kaifeng 475004, China
*
Authors to whom correspondence should be addressed.
Atmosphere 2020, 11(1), 32; https://doi.org/10.3390/atmos11010032
Received: 6 November 2019 / Revised: 22 December 2019 / Accepted: 24 December 2019 / Published: 27 December 2019
(This article belongs to the Special Issue Climate Change, Climatic Extremes, and Human Societies in the Past)
Complex temperature processes are the coupling results of natural and human processes, but few studies focused on the interactive effects between natural and human systems. Based on the dataset for temperature during the period of 1980–2012, we analyzed the complexity of temperature by using the Correlation Dimension (CD) method. Then, we used the Geogdetector method to examine the effects of factors and their interactions on the temperature process in the Yangtze River Delta (YRD). The main conclusions are as follows: (1) the temperature rose 1.53 °C; and, among the dense areas of population and urban, the temperature rose the fastest. (2) The temperature process was more complicated in the sparse areas of population and urban than in the dense areas of population and urban. (3) The complexity of temperature dynamics increased along with the increase of temporal scale. To describe the temperature dynamic, at least two independent variables were needed at a daily scale, but at least three independent variables were needed at seasonal and annual scales. (4) Each driving factor did not work alone, but interacted with each other and had an enhanced effect on temperature. In addition, the interaction between economic activity and urban density had the largest influence on temperature. View Full-Text
Keywords: correlation dimension method; Geogdetector method; interaction effect; multi-scale correlation dimension method; Geogdetector method; interaction effect; multi-scale
Show Figures

Figure 1

MDPI and ACS Style

Zhou, C.; Zhu, N.; Xu, J.; Yang, D. The Contribution Rate of Driving Factors and Their Interactions to Temperature in the Yangtze River Delta Region. Atmosphere 2020, 11, 32.

Show more citation formats Show less citations formats
Note that from the first issue of 2016, MDPI journals use article numbers instead of page numbers. See further details here.

Article Access Map by Country/Region

1
Back to TopTop